"""simple docstring"""



from typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__:Any									=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:List[Any]									=			{
    """huggingface/time-series-transformer-tourism-monthly""": (
        """https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json"""
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}






class  snake_case__      (       snake_case_       ):
 _snake_case       :					str							  =						"""time_series_transformer"""
 _snake_case       :					int							  =						{
     """hidden_size""": """d_model""",
     """num_attention_heads""": """encoder_attention_heads""",
     """num_hidden_layers""": """encoder_layers""",
 }


 def __init__( self    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = "student_t"    ,		lowerCamelCase = "nll"    ,		lowerCamelCase = 1    ,		lowerCamelCase = [1, 2, 3, 4, 5, 6, 7]    ,		lowerCamelCase = "mean"    ,		lowerCamelCase = 0    ,		lowerCamelCase = 0    ,		lowerCamelCase = 0    ,		lowerCamelCase = 0    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = 32    ,		lowerCamelCase = 32    ,		lowerCamelCase = 2    ,		lowerCamelCase = 2    ,		lowerCamelCase = 2    ,		lowerCamelCase = 2    ,		lowerCamelCase = True    ,		lowerCamelCase = "gelu"    ,		lowerCamelCase = 64    ,		lowerCamelCase = 0.1    ,		lowerCamelCase = 0.1    ,		lowerCamelCase = 0.1    ,		lowerCamelCase = 0.1    ,		lowerCamelCase = 0.1    ,		lowerCamelCase = 100    ,		lowerCamelCase = 0.02    ,		lowerCamelCase=True    ,		**lowerCamelCase    ,		):
        # time series specific configuration
        __a       =    prediction_length
        __a       =    context_length or prediction_length
        __a       =    distribution_output
        __a       =    loss
        __a       =    input_size
        __a       =    num_time_features
        __a       =    lags_sequence
        __a       =    scaling
        __a       =    num_dynamic_real_features
        __a       =    num_static_real_features
        __a       =    num_static_categorical_features
        if cardinality and num_static_categorical_features > 0:
               if len(lowerCamelCase						) != num_static_categorical_features:
                      raise ValueError(
                          "The cardinality should be a list of the same length as `num_static_categorical_features`"						)
               __a       =    cardinality
        else:
               __a       =    [0]
        if embedding_dimension and num_static_categorical_features > 0:
               if len(lowerCamelCase						) != num_static_categorical_features:
                      raise ValueError(
                          "The embedding dimension should be a list of the same length as `num_static_categorical_features`"						)
               __a       =    embedding_dimension
        else:
               __a       =    [min(50    ,		(cat + 1) // 2						) for cat in self.cardinality]
        __a       =    num_parallel_samples

        # Transformer architecture configuration
        __a       =    input_size * len(lowerCamelCase						) + self._number_of_features
        __a       =    d_model
        __a       =    encoder_attention_heads
        __a       =    decoder_attention_heads
        __a       =    encoder_ffn_dim
        __a       =    decoder_ffn_dim
        __a       =    encoder_layers
        __a       =    decoder_layers

        __a       =    dropout
        __a       =    attention_dropout
        __a       =    activation_dropout
        __a       =    encoder_layerdrop
        __a       =    decoder_layerdrop

        __a       =    activation_function
        __a       =    init_std

        __a       =    use_cache

        super().__init__(is_encoder_decoder=lowerCamelCase    ,		**lowerCamelCase						)






 @property
 def 						a__   ( self						):
        return (
            sum(self.embedding_dimension						)
            + self.num_dynamic_real_features
            + self.num_time_features
            + self.num_static_real_features
            + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
        )

"""simple docstring"""



from __future__ import annotations

from math import pi



def 	_lowerCamelCase(	a   ,    a   ,    a				):

       if (inductance, frequency, reactance).count(0				) != 1:
              raise ValueError("One and only one argument must be 0"				)
       if inductance < 0:
              raise ValueError("Inductance cannot be negative"				)
       if frequency < 0:
              raise ValueError("Frequency cannot be negative"				)
       if reactance < 0:
              raise ValueError("Inductive reactance cannot be negative"				)
       if inductance == 0:
              return {"inductance": reactance / (2 * pi * frequency)}
       elif frequency == 0:
              return {"frequency": reactance / (2 * pi * inductance)}
       elif reactance == 0:
              return {"reactance": 2 * pi * frequency * inductance}
       else:
              raise ValueError("Exactly one argument must be 0"				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
